With the abundance of multimedia data made available through various means in general and the Internet and world-wide web (WWW) in particular, there is a need for effective ways of searching for, and management of, such multimedia data. Searching, organizing and management of multimedia data in general and video data in particular may be challenging at best due to the difficulty of representing and comparing the information embedded in the video content, and due to the scale of information that needs to be checked. Moreover, when it is necessary to find a content of video by means of textual query, prior art cases revert to various metadata that textually describe the content of the multimedia data. However, such content may be abstract and complex by nature and not necessarily adequately defined by the existing and/or attached metadata.
The rapidly increasing multimedia databases, accessible for example through the Internet, call for the application of new methods of representation of information embedded in video content. Searching for multimedia in general and for video data in particular is challenging due to the huge amount of information that has to be priory indexed, classified and clustered. Moreover, prior art techniques revert to model-based methods to define and/or describe multimedia data. However, by its very nature, the structure of such multimedia data may be too abstract and/or complex to be adequately represented by means of metadata. The difficulty arises in cases where the target sought for multimedia data is not adequately defined in words, or by the respective metadata of the multimedia data. For example, it may be desirable to locate a car of a particular model in a large database of video clips or segments. In some cases the model of the car would be part of the metadata but in many cases it would not. Moreover, the car may be at angles different from the angles of a specific photograph of the car that is available as a search item. Similarly, if a piece of music, as in a sequence of notes, is to be found, it is not necessarily the case that in all available content the notes are known in their metadata form, or for that matter, the search pattern may just be a brief audio clip.
A system implementing a computational architecture (hereinafter “the Architecture”) that is based on a PCT patent application publication number WO 2007/049282 and published on May 3, 2007, entitled “A Computing Device, a System and a Method for Parallel Processing of Data Streams”, assigned to common assignee, is hereby incorporated by reference for all the useful information it contains. Generally, the Architecture consists of a large ensemble of randomly, independently, generated, heterogeneous processing cores, mapping in parallel data-segments onto a high-dimensional space and generating compact signatures for classes of interest.
A vast amount of multimedia content exists today, whether available on the web or on private networks, having partial or full metadata that describes the content. When new content is added, it is a challenge to provide metadata that is accurate because of the plurality of metadata that may be potentially associated with a multimedia data element. Trying to do so manually is a tedious task and impractical in view of the amount of multimedia content being generated daily. Even more challenging is the matching between different multimedia content that represents the same, similar, or related concepts and/or information from different perspectives. For example, an image of the Washington Memorial in Washington D.C., may be taken from different angles, from different distances, in different lighting conditions, and at different positions of the camera, so that while in one photograph the Memorial is diagonal to the picture it is horizontal in another.
It would be therefore advantageous to provide a solution to overcome the limitations of the prior art described hereinabove.